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enrichr_analysis

Analyze gene lists to identify overrepresented biological terms, pathways, and functions across multiple databases using statistical enrichment analysis.

Instructions

Perform gene set enrichment analysis using Enrichr with support for multiple gene set libraries. Use this tool when you need to:

  • analyze gene functions

  • test enrichment across different databases

  • find biological processes/pathways/diseases

  • perform functional enrichment

  • analyze gene sets

  • identify overrepresented terms

  • run enrichment analysis

  • perform gene ontology analysis

  • test for enriched biological terms

  • analyze gene list functionality across multiple databases. Returns only statistically significant terms (adjusted p < 0.05) to reduce context usage. Supports GO, pathways, disease, tissue, drug, and many other gene set libraries available in Enrichr.

This server is configured with the following default libraries:

  • GO_Biological_Process_2025: Gene Ontology terms describing biological objectives accomplished by gene products.

  • KEGG_2021_Human: Metabolic and signaling pathways from Kyoto Encyclopedia of Genes and Genomes for human.

  • Reactome_2022: Curated and peer-reviewed pathways from Reactome covering signaling, metabolism, gene expression, and disease.

  • MSigDB_Hallmark_2020: Hallmark gene sets representing well-defined biological states and processes from MSigDB.

  • ChEA_2022: ChIP-seq experiments from GEO, ENCODE, and publications identifying transcription factor-gene interactions from human and mouse.

  • GWAS_Catalog_2023: Genome-wide association study results from NHGRI-EBI GWAS Catalog linking genes to traits.

  • Human_Phenotype_Ontology: Standardized vocabulary of phenotypic abnormalities associated with human diseases.

  • STRING_Interactions_2023: Protein interactions from STRING database including experimental and predicted.

  • DrugBank_2022: Drug targets from DrugBank including approved drugs and experimental compounds.

  • CellMarker_2024: Manually curated cell type markers from CellMarker database for human and mouse.

The model should select the most relevant library/libraries from the list below based on the user's query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
genesYesList of gene symbols to analyze for enrichment (e.g., ['TP53', 'BRCA1', 'EGFR'])
librariesNoList of Enrichr libraries to use for analysis. If not specified, the configured defaults will be used. Available options include: 'ChEA_2022', 'ChEA_2016', 'ChEA_2013', 'ENCODE_TF_ChIP-seq_2015', 'ENCODE_TF_ChIP-seq_2014', 'ENCODE_Histone_Modifications_2015', 'ENCODE_Histone_Modifications_2013', 'Transcription_Factor_PPIs', 'TRANSFAC_and_JASPAR_PWMs', 'Genome_Browser_PWMs', 'MotifMap', 'ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X', 'TF_Perturbations_Followed_by_Expression', 'TF-LOF_Expression_from_GEO', 'PPI_Hub_Proteins', 'CORUM', 'KEGG_2021_Human', 'KEGG_2019_Human', 'KEGG_2019_Mouse', 'KEGG_2016', 'KEGG_2015', 'KEGG_2013', 'Reactome_2022', 'Reactome_2016', 'Reactome_2013', 'WikiPathways_2023_Human', 'WikiPathways_2021_Human', 'WikiPathways_2019_Human', 'WikiPathways_2019_Mouse', 'WikiPathways_2016', 'WikiPathways_2015', 'WikiPathways_2013', 'BioCarta_2016', 'BioCarta_2015', 'BioCarta_2013', 'HumanCyc_2016', 'HumanCyc_2015', 'NCI-Nature_2016', 'NCI-Nature_2015', 'Panther_2016', 'Panther_2015', 'MSigDB_Hallmark_2020', 'BioPlanet_2019', 'NURSA_Human_Endogenous_Complexome', 'hu.MAP', 'GO_Biological_Process_2025', 'GO_Biological_Process_2023', 'GO_Biological_Process_2021', 'GO_Biological_Process_2018', 'GO_Biological_Process_2017', 'GO_Biological_Process_2015', 'GO_Biological_Process_2013', 'GO_Molecular_Function_2025', 'GO_Molecular_Function_2023', 'GO_Molecular_Function_2021', 'GO_Molecular_Function_2018', 'GO_Molecular_Function_2017', 'GO_Molecular_Function_2015', 'GO_Molecular_Function_2013', 'GO_Cellular_Component_2025', 'GO_Cellular_Component_2023', 'GO_Cellular_Component_2021', 'GO_Cellular_Component_2018', 'GO_Cellular_Component_2017', 'GO_Cellular_Component_2015', 'GO_Cellular_Component_2013', 'Human_Phenotype_Ontology', 'MGI_Mammalian_Phenotype_2021', 'MGI_Mammalian_Phenotype_2017', 'MGI_Mammalian_Phenotype_2013', 'MGI_Mammalian_Phenotype_Level_3', 'MGI_Mammalian_Phenotype_Level_4', 'Human_Gene_Atlas', 'Anatomy_AutoRIF', 'Anatomy_AutoRIF_Predicted_Z-score', 'Jensen_TISSUES', 'Jensen_COMPARTMENTS', 'Jensen_DISEASES', 'ARCHS4_Tissues', 'ARCHS4_Cell-lines', 'Uberon_Cross_Species_Phenotype_Ontology', 'GWAS_Catalog_2023', 'GWAS_Catalog_2019', 'UK_Biobank_GWAS_v1', 'ClinVar_2019', 'PheWeb_2019', 'DisGeNET', 'PhenGenI_Association_2021', 'Orphanet_Augmented_2021', 'Rare_Diseases_AutoRIF_Gene_Lists', 'Rare_Diseases_AutoRIF_ARCHS4_Predictions', 'Rare_Diseases_GeneRIF_Gene_Lists', 'Rare_Diseases_GeneRIF_ARCHS4_Predictions', 'DrugBank_2022', 'DrugBank_2018', 'DSigDB', 'Drug_Perturbations_from_GEO_2014', 'Drug_Perturbations_from_GEO_down', 'Drug_Perturbations_from_GEO_up', 'LINCS_L1000_Chem_Pert_down', 'LINCS_L1000_Chem_Pert_up', 'LINCS_L1000_Ligand_Perturbations_down', 'LINCS_L1000_Ligand_Perturbations_up', 'Drug_Matrix', 'HMS_LINCS_KinomeScan', 'Proteomics_Drug_Atlas_2023', 'Virus_Perturbations_from_GEO_down', 'Virus_Perturbations_from_GEO_up', 'VirusMINT', 'Disease_Perturbations_from_GEO_down', 'Disease_Perturbations_from_GEO_up', 'Disease_Signatures_from_GEO_down_2014', 'Disease_Signatures_from_GEO_up_2014', 'Disease_Signatures_from_GEO_Manual_down', 'Disease_Signatures_from_GEO_Manual_up', 'COVID-19_Related_Gene_Sets', 'COVID-19_Related_Gene_Sets_2021', 'HDSigDB_Human_2021', 'HDSigDB_Mouse_2021', 'OMIM_Disease', 'OMIM_Expanded', 'DepMap_WG_CRISPR_Screens_Broad_CellLines_2019', 'DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019', 'Achilles_fitness_decrease', 'Achilles_fitness_increase', 'GeneSigDB', 'GWASdb_2023', 'MAGMA_Drugs_and_Diseases', 'MAGNET_2023', 'GeDiPNet_2023', 'GTEx_Tissue_Expression_Down', 'GTEx_Tissue_Expression_Up', 'GTEx_Tissue_Sample_Gene_Expression_Profiles_down', 'GTEx_Tissue_Sample_Gene_Expression_Profiles_up', 'GTEx_Aging_Signatures_2021', 'Allen_Brain_Atlas_10x_scRNA_2021', 'Allen_Brain_Atlas_down', 'Allen_Brain_Atlas_up', 'Tabula_Muris', 'Tabula_Sapiens', 'Azimuth_Cell_Types_2021', 'Azimuth_Cell_Types_Top5_Markers', 'CellMarker_2024', 'CellMarker_Augmented_2021', 'PanglaoDB_Augmented_2021', 'Descartes_Cell_Types_and_Tissue_2021', 'HuBMAP_ASCTplusB_augmented_2022', 'FANTOM6_lncRNA_KD_DEGs', 'CCLE_Proteomics_2020', 'ProteomicsDB_2020', 'HPA_Protein_Atlas_2023', 'TargetScan_microRNA_2017', 'miRTarBase_2017', 'miRTarBase_2022', 'MiRDB_2019', 'Epigenomics_Roadmap_HM_ChIP-seq', 'ENCODE_Chromatin_Accessibility_2023', 'ReMap_2022', 'ChIP_Atlas_2023', 'KEA_2015', 'KEA_2013', 'Kinase_Perturbations_from_GEO_down', 'Kinase_Perturbations_from_GEO_up', 'LINCS_L1000_Kinase_Perturbations_down', 'LINCS_L1000_Kinase_Perturbations_up', 'PhosphoSitePlus_2023', 'iPTMnet_2023', 'PTMsigDB_2023', 'LINCS_L1000_CRISPR_KO_Consensus_Sigs', 'CRISPR_GenomeWide_2023', 'L1000_Kinase_and_GPCR_Perturbations_down', 'L1000_Kinase_and_GPCR_Perturbations_up', 'LINCS_L1000_shRNA_Consensus_Sigs', 'TG_GATES_2020', 'ORCAtlas_2023', 'HMDB_Metabolites', 'Metabolomics_Workbench_2023', 'SMPDB_2023', 'Aging_Perturbations_from_GEO_down', 'Aging_Perturbations_from_GEO_up', 'GenAge_2023', 'Longevity_Map_2023', 'InterPro_Domains_2019', 'Pfam_Domains_2019', 'UniProt_Keywords_2023', 'Homologene', 'Enrichr_Users_Contributed_Lists_2020', 'Enrichr_Consensus_Top_100', 'Enrichr_Libraries_Most_Popular_Genes', 'Enrichr_Submissions_TF-Gene_Coocurrence', 'ARCHS4_Kinase_Coexp', 'ARCHS4_TF_Coexp', 'ARCHS4_IDG_Coexp', 'GeneRIF/ARCHS4_Human_Top_Predicted_Transcription_Factors', 'GeneRIF/ARCHS4_Mouse_Top_Predicted_Transcription_Factors', 'Rummagene_kinases', 'Rummagene_transcription_factors', 'Rummagene_signatures', 'AutoRIF', 'GeneRIF', 'GO_Biological_Process_AutoRIF', 'GO_Molecular_Function_AutoRIF', 'GO_Cellular_Component_AutoRIF', 'COSMIC_Cancer_Gene_Census', 'TCGA_Coexp_2023', 'TCGA_Mutations_2023', 'OncoKB_2023', 'Cancer_Cell_Line_Encyclopedia', 'GDSC_2023', 'Human_Cell_Landscape', 'Mouse_Cell_Atlas', 'scRNAseq_Datasets_2023', 'SingleCellSignatures_2023', 'Chromosome_Location', 'Chromosome_Location_hg19', 'STRING_Interactions_2023', 'BioGRID_2023', 'IntAct_2023', 'MINT_2023', 'PDB_Structural_Annotations', 'AlphaFold_2023', 'ImmuneSigDB', 'ImmPort_2023', 'Immunological_Signatures_MSigDB', 'ESCAPE', 'Developmental_Signatures_2023', 'Embryonic_Stem_Cell_Atlas_from_Pluripotency_Evidence', 'JASPAR_2022', 'HOCOMOCO_v11', 'SwissRegulon_2023', 'Cistrome_2023', 'MSigDB_Computational', 'MSigDB_Curated', 'SynGO_2022', 'SysMyo_2023', 'FlyBase_2023', 'WormBase_2023', 'HGNC_Gene_Families', 'NURBS_2023', 'ToppGene_2023', 'Bioplanet_2019', 'GTEx_eQTL', 'clinGen_2023', 'Alliance_Genome_2023', 'IDG_Drug_Targets_2023', 'Ligand_Receptor_Pairs_2023', 'IMPC_2023', 'KOMP2_2023', 'MGI_2023', 'DEPOD_2023', 'dbGAP_2023', 'UK_Biobank_2023', 'FinnGen_2023', 'Open_Targets_2023', 'PharmGKB_2023', 'STITCH_2023', 'L1000_Connectivity_Map_2023', 'CMAP_2023'.
descriptionNoOptional description for the gene listGene list for enrichment analysis
maxTermsNoMaximum number of terms to show per library. Use higher values (20-50) to capture more biological insights, especially for libraries with many significant terms.
formatNoOutput format: detailed, compact, minimaldetailed
outputFileNoOptional path to save complete results as TSV file. If specified, ALL significant terms will be saved to this file, regardless of maxTerms limit.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it filters results to 'only statistically significant terms (adjusted p < 0.05),' mentions context usage reduction, lists 10 default libraries with descriptions, and instructs the model to 'select the most relevant library/libraries.' However, it doesn't cover rate limits, error conditions, or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is overly verbose and poorly structured: it includes a redundant bulleted list of 10 similar use cases (e.g., 'analyze gene sets' and 'perform functional enrichment' are essentially the same), followed by an extensive list of 10 default libraries with descriptions that could be streamlined. The core information is buried, and many sentences don't earn their place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, no output schema, no annotations), the description is mostly complete: it covers purpose, usage, behavioral constraints (significance filtering), and provides library context. However, it lacks details on output structure, error handling, and performance characteristics, which would be helpful for a tool with no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds minimal parameter semantics beyond the schema: it implies the 'libraries' parameter should be selected based on relevance to the query and lists 10 default libraries with brief explanations, but doesn't provide additional meaning for other parameters like 'genes,' 'maxTerms,' or 'format.'

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs 'gene set enrichment analysis using Enrichr with support for multiple gene set libraries.' It specifies the verb ('perform'), resource ('Enrichr'), and scope ('multiple gene set libraries'), and distinguishes from the sibling tool 'go_bp_enrichment' by emphasizing broader library support beyond just GO Biological Process.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance with a bulleted list of 10 specific scenarios (e.g., 'analyze gene functions,' 'test enrichment across different databases'), includes when-not guidance by stating 'Returns only statistically significant terms (adjusted p < 0.05) to reduce context usage,' and implicitly suggests alternatives by mentioning the sibling tool 'go_bp_enrichment' for more focused GO analysis.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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